提出多尺度时空网络，提升机器人动态路径规划的适应性与路径质量。
GundamQ: Multi-Scale Spatio-Temporal Representation Learning for Robust Robot Path Planning
- 分层提取时空特征，建模从瞬时到长期的时间依赖关系。
- 成功率达91.2%，路径质量提升21.7%，优于现有方法。
- 适合需要高鲁棒性的复杂动态环境机器人应用。
在动态不确定环境中，机器人路径规划需精准的时空环境理解与部分可观情况下的稳健决策。然而，当前基于深度强化学习的规划方法存在两大根本局限：(1) 多尺度时间依赖建模不足，导致动态场景下适应性较差；(2) 探索与利用平衡效率低，影响路径质量。为此，我们提出GundamQ：一种用于机器人路径规划的多尺度时空Q网络。该框架包含两个核心模块：(i) 空间-时间感知模块，分层提取多粒度空间特征与从瞬时到长期时间跨度的多尺度时间依赖，从而提升动态环境中的感知精度；(ii) 自适应策略优化模块，在训练中平衡探索与利用，并通过约束策略更新优化路径平滑性与碰撞概率。在动态环境中的实验表明，GundamQ成功率达到91.2%，路径质量提升21.7%，显著优于现有最先进方法。
原文摘要 · Abstract (English)
In dynamic and uncertain environments, robotic path planning demands accurate spatiotemporal environment understanding combined with robust decision-making under partial observability. However, current deep reinforcement learning-based path planning methods face two fundamental limitations: (1) insufficient modeling of multi-scale temporal dependencies, resulting in suboptimal adaptability in dynamic scenarios, and (2) inefficient exploration-exploitation balance, leading to degraded path quality. To address these challenges, we propose GundamQ: A Multi-Scale Spatiotemporal Q-Network for Robotic Path Planning. The framework comprises two key modules: (i) the Spatiotemporal Perception module, which hierarchically extracts multi-granularity spatial features and multi-scale temporal dependencies ranging from instantaneous to extended time horizons, thereby improving perception accuracy in dynamic environments; and (ii) the Adaptive Policy Optimization module, which balances exploration and exploitation during training while optimizing for smoothness and collision probability through constrained policy updates. Experiments in dynamic environments demonstrate that GundamQ achieves a 15.3\% improvement in success rate and a 21.7\% increase in overall path quality, significantly outperforming existing state-of-the-art methods.
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